18 citations · 18 across the 3 of their papers we have counts for
6 papers
Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis
Dennis Heijmans, H. Çağrı Bilgi, Rui Wang +1
Financial transaction networks are naturally represented as multigraphs, where repeated transactions between the same pair of accounts are modeled as distinct edges. Recently propo…
Random Probing for Structural Self-Interactions in Graph Neural Networks
Saku Peltonen, H. Çağrı Bilgi, Kubilay Atasu
Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, subgraph centrality, and heat…
Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
H. Çağrı Bilgi, Kemal Mutluergil, Ceren Yıldırım +2
Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks cannot share account and trans…
Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detection
Selin Ceydeli, Rui Wang, Kubilay Atasu
Subgraph pattern detection is a fundamental graph mining task central to anti-money laundering (AML) in finance. However, state-of-the-art graph neural network (GNN)-based methods…
MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes
H. Çağrı Bilgi, Kubilay Atasu
Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world systems. In these graphs, effective…
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
Jovan Blanuša, Maximo Cravero Baraja, Andreea Anghel +4
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patte…